Build a Reasoning Model From Scratch Is Out
A year and a half, countless experiments, and hundreds of pages later… today was one of my favorite days as an engineer and author. Build a Reasoning Model (From Scratch) is finally out, and my first copies just arrived!
440 full-color pages.
A huge thank you to everyone who joined me as an early reader and reviewer over the past 1.5 years. I hope this is a worthy sequel to Build a Large Language Model (From Scratch).
If you are wondering what it covers, it walks you through implementing modern reasoning techniques from scratch on top of a small Qwen3 base model, with a focus on:
- inference scaling
- reinforcement learning
- distillation
While Build a Large Language Model (From Scratch) focuses on building and pre-training an LLM, this book picks up where that one leaves off and covers what comes next.
(If you enjoy model architecture details, don’t worry, the complete Qwen3 architecture is also implemented from scratch and explained in the appendix.)
The book is now shipping from the publisher:
Build a Reasoning Model (From Scratch)
And it is also available for preorder on Amazon. Shipping is expected to begin there in a few weeks:
Amazon preorder for Build a Reasoning Model (From Scratch)
I hope it serves as a useful resource for anyone who wants to understand how reasoning models, which are now a key component of many modern AI agents, work under the hood.
Thanks again to everyone who helped make this book possible! Happy reading!
Source: lightly edited website version of my Substack note.
Read Next
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Local Open-Weight LLMs in Coding Harnesses
Short note on trying local open-weight LLMs across Qwen-Code, Codex, and Claude Code harnesses.
GLM-5.2 and IndexShare for Long-Context Sparse Attention
Short note on GLM-5.2, an open-weight GLM update that keeps the GLM-5 sparse MoE backbone and adds IndexShare for cheaper 1M-token DSA inference.
